• Dr.M. Indra Devi
• S.Ummugulthum Natchiar
• S.K.Sree Preethi

Abstract: Earthquake prediction is a critical challenge in seismology, primarily due to the intricate and non-linear characteristics of seismic events. While traditional forecasting methods have their limitations, deep learning techniques have proven to be effective and promising alternatives. This paper delivers a comprehensive review of several advanced deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, among others, that are being leveraged for earthquake prediction. Furthermore, we provide a definitive comparative analysis of these models, underscoring their strengths, challenges, and performance metrics. Finally, we assert that the future lies in the integration of real-time seismic data with these deep-learning methods, which will significantly enhance the accuracy of earthquake predictions

Earthquake Prediction, Deep Learning, Machine Learning, CNNs, RNNs, LSTM, Seismic Hazard, Earthquake Forecasting, Neural Networks

"Deep Learning Techniques For Earthquake Prediction: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j170-j173, March-2025, Available :http://www.jetir.org/papers/JETIR2503924.pdf

"Deep Learning Techniques For Earthquake Prediction: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj170-j173, March-2025, Available at : http://www.jetir.org/papers/JETIR2503924.pdf